Amazon Technologies, Inc.

Xing Liu

Yue Liu

Pradeep Natarajan

Qingfeng Yu

A system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A device captures a video frame including text characters. An edge detection filter is applied to the frame to determine gradient features in perpendicular directions. An 'edge map' is created from the gradient features, and points along edges in the edge map are identified. Edge transition widths are determined at each of the edge points based in local intensity minimum and maximum on opposite sides of the respective edge point in the frame. Sharper edges have smaller edge transition widths than blurry images. Statistics are determined from the edge transition widths, and the statistics are processed by a trained classifier to determine if the frame is or is not sufficiently sharp for text processing.

Sharpness-based frame selection for OCR

A process for training and optimizing a system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A set of image frames are subjectively labelled based on a comparison of each frame before and after binarization to determine to what degree text is recognizable in the binary image. A plurality of different sharpness feature metrics are generated based on the original frame. A classifier is then trained using the feature metrics and the subjective labels. The feature metrics are then tested for accuracy and/or correlation with subjective labelling data. The set of feature metrics may be refined based on which metrics produce the best results.

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West Lafayette, IN, United States of America

Xing Liu